A vehicle-mounted multi-container system resource configuration method, device, equipment and medium

Abstract

The application discloses a vehicle-mounted multi-container system resource configuration method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring system resource usage data corresponding to a container according to a service type of a service borne by the container by using a data acquisition strategy corresponding to the service type; performing data analysis and processing on the system resource usage data to obtain processed data; determining scene information currently corresponding to the container by using an inference engine based on the processed data and a preset rule base; the preset rule base is a rule base constructed according to resource occupation characteristics of different scenes; determining a resource configuration strategy corresponding to the container under the scene information by querying a pre-constructed correspondence relationship between scenes and resource configurations; and performing resource configuration on the container according to the resource configuration strategy currently corresponding to each container. The method can adapt to different application scenarios and improve the overall performance of a vehicle-mounted system.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for resource allocation in a vehicle-mounted multi-container system. Background Technology

[0002] Most mainstream in-vehicle infotainment (IVI) systems currently employ a System-on-Chip (SoC) solution. The SoC integrates modules such as a modem, Wi-Fi, Bluetooth (BT), and GPS, providing functions like external network communication and local area network communication. It also boasts powerful CPUs (Central Processing Units), GPUs (Graphics Processing Units), and various DSPs (Digital Signal Processors). However, for a long time, these in-vehicle systems have not fully utilized these hardware resources, instead relying on independent telematics boxes (TBoxes) and instrument clusters for communication and instrument functions, resulting in a significant waste of SoC hardware resources.

[0003] Therefore, this paper proposes implementing the TBox and metering systems based on a System-on-Chip (SOC) system to improve SOC system resource utilization and reduce costs. To prevent the IVI, TBox, and metering systems, implemented on the same hardware, from interfering with each other, virtualization technology, such as containers, is introduced to run the IVI, TBox, and metering services in their respective isolated systems. In existing technologies, the amount of resources available to each container is configured on the host system based on experience, i.e., a fixed resource configuration scheme is used. However, systems operate dynamically; different scenarios and states lead to dynamic changes in the resource requirements of different containers. Fixed resource configuration schemes can only adapt to a limited number of scenarios. In most cases, the resource allocation between the entire system and containers is unreasonable and fails to improve the performance of multi-container systems. In some extreme cases, it can even severely impact user experience and system stability. Therefore, how to improve the performance and user experience of each container as much as possible without affecting the overall system stability is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for configuring resources in a vehicle-mounted multi-container system, which can adapt to different application scenarios and improve the overall performance of the vehicle-mounted system. The specific solution is as follows:

[0005] Firstly, this application discloses a method for configuring resources in a vehicle-mounted multi-container system, including:

[0006] Based on the service type of the service carried by the container, the system resource usage data corresponding to the container is obtained using the data acquisition strategy corresponding to the service type.

[0007] The system resource usage data is analyzed and processed to obtain the processed data;

[0008] Based on the processed data and the preset rule base, the inference engine is used to determine the current scene information corresponding to the container; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenes.

[0009] By querying the pre-built correspondence between scenarios and resource configurations, the resource configuration strategy corresponding to the container under the scenario information is determined;

[0010] Configure resources for each container according to the resource configuration strategy currently applicable to each container.

[0011] Optionally, before determining the current scene information corresponding to the container using the inference engine based on the processed data and the preset rule base, the method further includes:

[0012] Acquire historical data from multiple different scenarios;

[0013] Based on the correspondence between historical data and scenarios, analyze the resource consumption characteristics of different scenarios, and construct scenario recognition rules for different scenarios based on the analysis results;

[0014] The scene recognition rules are verified, and the scene recognition rules are adjusted if the verification result is unsuccessful.

[0015] The preset rule library is generated based on the scene recognition rules corresponding to all scenarios.

[0016] Optionally, obtaining the system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type includes:

[0017] According to a preset cycle, the system resource usage data corresponding to the container is obtained periodically using a data acquisition strategy corresponding to the business type.

[0018] Optionally, before obtaining the system resource usage data corresponding to the container using the data acquisition strategy corresponding to the business type, the method further includes:

[0019] By querying the pre-built correspondence between business and resource items, the target resource item corresponding to the business type of the business on the container is determined;

[0020] The data acquisition strategy is generated based on the target resource item.

[0021] Optionally, before configuring resources for each container according to the resource configuration strategy currently corresponding to each container, the method further includes:

[0022] Based on the processed data and the resource configuration strategy, determine whether the container meets the resource configuration conditions;

[0023] If the resource configuration conditions are met, then the operation of configuring resources for each container according to the current resource configuration strategy for each container is executed.

[0024] Optionally, configuring resources for containers according to the resource configuration policy currently corresponding to each container includes:

[0025] Determine the resource configuration priority for each container based on the priority of the services running on the container.

[0026] Based on the resource configuration strategy corresponding to each container, resources are configured for each container in sequence according to the resource configuration priority.

[0027] Optionally, the in-vehicle multi-container system includes a first container for running an in-vehicle entertainment system, a second container for running a vehicle networking system, and a third container for running a vehicle dashboard.

[0028] Among them, the priority of the car dashboard is higher than the priority of the vehicle networking system, the priority of the vehicle networking system is higher than the priority of the in-vehicle entertainment system; the resource configuration priority of the third container is higher than the resource configuration priority of the second container, and the resource configuration priority of the second container is higher than the resource configuration priority of the first container.

[0029] Secondly, this application discloses a vehicle-mounted multi-container system resource configuration device, comprising:

[0030] The resource monitoring module is used to obtain system resource usage data corresponding to the container based on the service type of the service carried by the container and using a data acquisition strategy corresponding to the service type.

[0031] The data analysis and processing module is used to perform data analysis and processing on the system resource usage data to obtain processed data;

[0032] The scene identification module is used to determine the scene information corresponding to the container based on the processed data and the preset rule base using an inference engine; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenes.

[0033] The resource configuration strategy determination module is used to determine the resource configuration strategy corresponding to the container under the scenario information by querying the pre-built scenario-relationship.

[0034] The resource configuration module is used to configure resources for containers according to the resource configuration strategy currently applicable to each container.

[0035] Thirdly, this application discloses an electronic device, including:

[0036] Memory, used to store computer programs;

[0037] A processor is used to execute the computer program to implement the aforementioned in-vehicle multi-container system resource configuration method.

[0038] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned in-vehicle multi-container system resource configuration method.

[0039] In this application, based on the business type of the service carried by the container, system resource usage data corresponding to the container is obtained using a data acquisition strategy corresponding to the business type; the system resource usage data is analyzed and processed to obtain processed data; based on the processed data and a preset rule base, an inference engine is used to determine the current scenario information corresponding to the container; the preset rule base is a rule base constructed according to the resource occupancy characteristics of different scenarios; by querying the pre-constructed scenario-relationship with resource configuration, the resource configuration strategy corresponding to the container under the scenario information is determined; and the container is configured with resources according to the resource configuration strategy corresponding to each container. It can be seen that different data acquisition strategies are designed for different types of services, and system resource usage data that can characterize the operation characteristics of the service is obtained through the data acquisition strategies. After analyzing the system resource usage data, the rule base constructed according to the resource occupancy characteristics of different scenarios and the inference engine are used to identify the current scenario of the container to obtain scenario information. Then, a resource configuration strategy adapted to the scenario is determined based on the scenario information, thereby realizing the adaptive configuration of resources in the vehicle multi-container system. This solves the problem of the current fixed resource configuration of the vehicle multi-container system, which cannot adapt to different scenarios, and improves the overall performance of the vehicle system. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0041] Figure 1 A flowchart of a resource configuration method for an on-board multi-container system provided in this application;

[0042] Figure 2 This application provides a schematic diagram of a specific vehicle-mounted multi-container system resource configuration system;

[0043] Figure 3 A flowchart of a specific resource monitoring method provided in this application;

[0044] Figure 4 A schematic diagram of a specific scene recognition expert system structure is provided for this application;

[0045] Figure 5 A flowchart illustrating a specific scene identification method provided in this application;

[0046] Figure 6 This application provides a specific flowchart for generating a rule base.

[0047] Figure 7 This application provides a specific resource allocation flowchart;

[0048] Figure 8 This application provides a schematic diagram of the structure of a vehicle-mounted multi-container system resource allocation device;

[0049] Figure 9 This application provides a structural diagram of an electronic device. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In existing technologies, the amount of resources available to each container on the host system is configured based on experience, i.e., a fixed resource configuration scheme is adopted. However, the system operates dynamically, and the resource requirements of different containers change dynamically depending on different scenarios and states. A fixed resource configuration scheme can only adapt to a limited number of scenarios. In most cases, the resource allocation between the entire system and containers is unreasonable and cannot improve the performance of multi-container systems. In some extreme cases, it can even seriously affect the user experience and even the stability of the system. To overcome the above technical problems, this application proposes a resource configuration method for an in-vehicle multi-container system that can adapt to different application scenarios and improve the overall performance of the in-vehicle system.

[0052] This application discloses a resource configuration method for an on-board multi-container system. (See also...) Figure 1 As shown, the method may include the following steps:

[0053] Step S11: Based on the service type of the service carried by the container, obtain the system resource usage data corresponding to the container using the data acquisition strategy corresponding to the service type.

[0054] In this embodiment, container system resources are monitored. The system within a container uses system resources allocated to that container (such as CPU, memory, GPU, etc.). Resources are isolated between containers; therefore, the usage of container system resources can serve as identification data for container system scenarios. The container system resource monitoring module runs within each container and formulates strategies for acquiring system resource data based on different services within the container.

[0055] In some embodiments, before acquiring the system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type, the process may further include: determining the target resource item corresponding to the business type of the business on the container by querying a pre-built business-resource item correspondence; and generating the data acquisition strategy based on the target resource item. That is, different data acquisition strategies target different types of resource parameters. It is understood that some businesses are computationally intensive, while others may be interactive, resulting in different consumption or requirements of system resources. Therefore, the criteria for identifying system application scenarios also differ. Some containers may focus on CPU, while others may focus on memory, etc. For example, for the IVI business, the corresponding target resource items include CPU, MEM, and GPU resources, meaning that at least CPU, MEM, and GPU resource data need to be acquired; for the TBOX business, CPU and MEM data need to be acquired, primarily CPU resource data; for the Cluster business, CPU, MEM, and GPU data need to be acquired, primarily CPU and GPU resource data.

[0056] In some embodiments, obtaining system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type may include: periodically obtaining system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type according to a preset period. That is, periodically obtaining system resource usage data within the most recent time period in order to identify system scenarios in a timely manner.

[0057] Step S12: Perform data analysis and processing on the system resource usage data to obtain processed data.

[0058] Since the collected data is fragmented and discontinuous, in order to further analyze the scenarios based on the data, we first perform data analysis and processing on the resource usage data, such as statistical and averaging calculations, so that we can use this data to identify system scenarios.

[0059] In some embodiments, the data analysis and processing of the system resource usage data may include: performing data analysis and processing on all system resource usage data corresponding to the container acquired within a preset time period to obtain processed data. It is understood that during system operation, the resource monitoring module within each container will acquire real-time data on current resource usage at certain intervals, and the resource data processing module will process the system resource data to obtain data that can be used for system scenario identification, such as CPU utilization. The processing operations of the resource data processing module include, but are not limited to, averaging, statistical, and other operations.

[0060] Step S13: Based on the processed data and the preset rule base, use the inference engine to determine the current scene information corresponding to the container; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenes.

[0061] A rule base is pre-built based on the resource consumption characteristics of different scenarios. Scenarios can be categorized according to the business usage and system load. Different containers carry different businesses, and the same business can also have different scenarios, resulting in different system loads and resource usage. For example, using an IVI to play an app, play videos, or run games corresponds to different scenarios: IVI normal scenario, IVI high-load scenario, IVI ultra-high-load scenario, etc. Specific scenarios can include IVI-side idle scenario, IVI-side normal scenario, IVI-side high-load scenario, IVI ultra-high-load scenario, Cluster-side idle scenario, Cluster-side normal scenario, Cluster-side high-load scenario, etc. Based on the resource usage of various types of resources under each scenario, the resource consumption characteristics of different scenarios are analyzed, and identification rules for different scenarios are built accordingly and added to the rule base.

[0062] In some embodiments, before determining the current scenario information corresponding to the container using the inference engine based on the processed data and the preset rule base, the process may further include: acquiring historical data under multiple different scenarios, where historical data refers to the system resource usage data under different scenarios in the past; analyzing the resource occupancy characteristics of different scenarios based on the correspondence between historical data and scenarios, and constructing scenario recognition rules for different scenarios based on the analysis results; verifying the scenario recognition rules, and adjusting the scenario recognition rules if the verification result is unsuccessful; and generating the preset rule base based on the scenario recognition rules corresponding to all scenarios. Experts formulate rules based on the system scenario, combined with their own knowledge and experience, and then verify the rules. The inference engine is used to match the resource occupancy characteristics of the scenario based on the current resource usage data of the container system, and obtain resource configuration strategies or rules from the characteristics. Verification is to verify whether the rules can identify the scenario and whether the resource rules can be effectively implemented; for example, recording and video playback are high-load scenarios, and if the rules identify a normal scenario, the rule verification will fail.

[0063] Step S14: By querying the pre-built correspondence between scenarios and resource configurations, determine the resource configuration strategy corresponding to the container under the scenario information.

[0064] The resource requirements for different scenarios are predefined, and the correspondence between scenarios and resource configurations can be stored in the aforementioned pre-defined rule base. Based on the processed data and the pre-defined rule base, after the inference engine determines the current scenario information for the container, the resource configuration strategy for the container in the current scenario is determined using the scenario-relationship in the pre-defined rule base. In other words, after determining the scenario, the current demand for each resource item is queried, and the resource configuration strategy is obtained based on the demand for all types of resources. Different types of resource configurations can be configured separately and independently. For example, in a Cluster idle scenario and an IVI high-load scenario, resources need to be adjusted to allocate more resources to the IVI.

[0065] Step S15: Configure resources for each container according to the resource configuration strategy currently corresponding to each container.

[0066] Finally, resources are configured for the corresponding containers according to the generated resource configuration strategy. Specifically, the aforementioned containers can be LXC (Linux Container), a lightweight virtualization technology for the Linux platform used to achieve operating system-level virtualization based on the Linux platform. It has advantages such as relatively simple development, minimal overall system performance loss, and good system stability. Container resource configuration is specifically accomplished by calling the LXC API (Application Programming Interface). Therefore, by using rule-based system scenario identification to infer from real-time factual data of the multi-container system, the most suitable resource configuration scheme for the current system scenario is derived. Then, the system resource configuration module dynamically adjusts the multi-container system resource configuration, achieving adaptive configuration of in-vehicle multi-container system resources. This solves the problem of current in-vehicle multi-container system resource configuration being fixed and unable to adapt to more scenarios, greatly improving the overall performance and stability of the in-vehicle system.

[0067] In some embodiments, before configuring resources for containers according to the resource configuration strategy currently corresponding to each container, the process may further include: determining whether the container meets the resource configuration conditions based on the processed data and the resource configuration strategy; if the resource configuration conditions are met, then performing the operation of configuring resources for containers according to the resource configuration strategy currently corresponding to each container. It is understood that since each resource configuration will cause the system to re-adapt and adjust, resource configuration should not be too frequent. This embodiment can use methods such as statistical analysis, averaging, and weighting of resource usage over a period of time, until the data reaches the credibility required for scene identification within a certain period, i.e., several analyses meet the resource configuration conditions, before resource configuration is triggered.

[0068] In some embodiments, configuring resources for containers according to the resource configuration policy currently corresponding to each container may include: determining the resource configuration priority for each container based on the priority of the services on the container; and configuring resources for each container sequentially according to the resource configuration priority based on the resource configuration policy corresponding to each container. That is, since there are priorities among different services, resources are configured first for services with higher priority.

[0069] In this embodiment, the in-vehicle multi-container system may specifically include a first container for running an in-vehicle entertainment system, a second container for running a vehicle networking system, and a third container for running a vehicle dashboard. The vehicle dashboard has a higher priority than the vehicle networking system, which in turn has a higher priority than the in-vehicle entertainment system. The resource allocation priority of the third container is higher than that of the second container, which in turn has a higher priority than that of the first container. That is, the system resources of the vehicle dashboard are allocated first to meet its resource requirements, and the remaining resources are then allocated to the in-vehicle entertainment system.

[0070] As can be seen from the above, in this embodiment, based on the business type of the service carried by the container, system resource usage data corresponding to the business type is obtained using a data acquisition strategy corresponding to the business type; the system resource usage data is analyzed and processed to obtain processed data; based on the processed data and a preset rule base, an inference engine is used to determine the current scenario information corresponding to the container; the preset rule base is a rule base constructed according to the resource occupancy characteristics of different scenarios; by querying the pre-constructed scenario-relationship with resource configuration, the resource configuration strategy corresponding to the container under the scenario information is determined; and the container is configured with resources according to the resource configuration strategy corresponding to each container. It is evident that different data acquisition strategies are designed for different types of services, and system resource usage data that can characterize the operation characteristics of the service is obtained through the data acquisition strategies. After analyzing the system resource usage data, the rule base constructed in advance according to the resource occupancy characteristics of different scenarios, and the inference engine are used to identify the current scenario of the container to obtain scenario information. Then, a resource configuration strategy adapted to the scenario is determined based on the scenario information, thereby realizing the adaptive configuration of resources in the vehicle multi-container system. This solves the problem of the current fixed resource configuration of the vehicle multi-container system, which cannot adapt to different scenarios, and improves the overall performance of the vehicle system.

[0071] Accordingly, this application also discloses an in-vehicle multi-container resource configuration system, including a resource monitoring module, a system resource data processing module, a rule-based expert system, and a resource configuration module. The rule-based expert system includes a preset rule base. This expert system is used to determine the current scene information corresponding to the container based on processed data and the preset rule base, utilizing the expert system's inference engine. Furthermore, the expert system determines the resource configuration strategy corresponding to the container under the scene information by querying a pre-built correspondence between scenes and resource configurations. For example... Figure 2As shown, each container contains a resource monitoring module; the system resource data processing module, rule-based expert system, and resource configuration module are applied on the host side. The resource monitoring module sends real-time system resource data to the data processing module, which performs statistical, averaging, and other calculations on the data, and finally provides it to the system scene identification expert system.

[0072] For example Figure 3 As shown, the container system resource monitoring module runs within each container, and formulates strategies for acquiring system resource data for different services within the container; the container system resource monitoring module sends real-time system resource data to the system resource data processing module. Figure 4 As shown, the rule-based scene recognition expert system mainly consists of a rule base, a fact database, a reasoning system, and a user interface; Figure 5 The diagram shows the flowchart of the scene identification method. The rule base contains knowledge and rules provided by domain experts; the database stores factual data for reasoning and matching with the rule base; the reasoning engine executes reasoning, and the expert system finds a solution; the user interface feeds back the solution obtained from the reasoning to the user. Figure 6 The flowchart shown illustrates the rule base generation process. It utilizes historical data from multiple different scenarios; analyzes the resource consumption characteristics of different scenarios based on the correspondence between historical data and scenarios; constructs scenario recognition rules for different scenarios based on the analysis results; validates the scenario recognition rules and adjusts them if the validation result is unsuccessful; and generates a rule base based on the scenario recognition rules corresponding to all scenarios. The resource configuration module obtains the identification results from the scenario identification expert system, then converts the identification results into resource configuration policies, which are then distributed to the system through the container engine. This module is responsible for the resource configuration and management of all containers in the entire system. The flowchart is shown below. Figure 7 As shown, the parameters represent the identification results, and their validity is verified before matching resource strategies. The expert system combines factual data and rules to perform reasoning, ultimately deriving the identification results and identification credibility parameters, matching appropriate resource configuration strategies, and thus achieving adaptive configuration of resources in a multi-container system.

[0073] Accordingly, this application also discloses a vehicle-mounted multi-container system resource configuration device, see [link to relevant documentation]. Figure 8 As shown, the device includes:

[0074] Resource monitoring module 11 is used to obtain system resource usage data corresponding to the container based on the service type of the service carried by the container and using a data acquisition strategy corresponding to the service type.

[0075] Data analysis and processing module 12 is used to perform data analysis and processing on the system resource usage data to obtain processed data;

[0076] The scene identification module 13 is used to determine the scene information corresponding to the container based on the processed data and the preset rule base using an inference engine; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenes.

[0077] The resource configuration strategy determination module 14 is used to determine the resource configuration strategy corresponding to the container under the scenario information by querying the pre-built scenario-relationship.

[0078] The resource configuration module 15 is used to configure resources for containers according to the resource configuration strategy currently corresponding to each container.

[0079] As can be seen from the above, in this embodiment, based on the business type of the service carried by the container, system resource usage data corresponding to the business type is obtained using a data acquisition strategy corresponding to the business type; the system resource usage data is analyzed and processed to obtain processed data; based on the processed data and a preset rule base, an inference engine is used to determine the current scenario information corresponding to the container; the preset rule base is a rule base constructed according to the resource occupancy characteristics of different scenarios; by querying the pre-constructed scenario-relationship with resource configuration, the resource configuration strategy corresponding to the container under the scenario information is determined; and the container is configured with resources according to the resource configuration strategy corresponding to each container. It is evident that different data acquisition strategies are designed for different types of services, and system resource usage data that can characterize the operation characteristics of the service is obtained through the data acquisition strategies. After analyzing the system resource usage data, the rule base constructed in advance according to the resource occupancy characteristics of different scenarios and the inference engine are used to identify the current scenario of the container to obtain scenario information. Then, a resource configuration strategy adapted to the scenario is determined based on the scenario information, thereby realizing the adaptive configuration of resources in the vehicle multi-container system. This solves the problem of the current fixed resource configuration of the vehicle multi-container system, which cannot adapt to different scenarios, and improves the overall performance of the vehicle system.

[0080] In some specific embodiments, the preset rule base may specifically be generated by acquiring historical data under multiple different scenarios, analyzing the resource consumption characteristics of different scenarios based on the correspondence between historical data and scenarios, constructing scenario recognition rules for different scenarios based on the analysis results, verifying the scenario recognition rules, adjusting the scenario recognition rules when the verification result is unsuccessful, and finally generating the rule base based on the scenario recognition rules corresponding to the scenario.

[0081] In some specific embodiments, the resource monitoring module 11 may specifically include:

[0082] The system resource usage data acquisition unit is used to periodically acquire system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type according to a preset period.

[0083] In some specific embodiments, the vehicle-mounted multi-container system resource configuration device may specifically include:

[0084] The target resource item determination unit is used to determine the target resource item corresponding to the business type of the business on the container by querying a pre-built business-resource item correspondence before obtaining the system resource usage data corresponding to the container using the data acquisition strategy corresponding to the business type.

[0085] A data acquisition strategy generation unit is used to generate the data acquisition strategy based on the target resource item.

[0086] In some specific embodiments, the vehicle-mounted multi-container system resource configuration device may specifically include:

[0087] The resource configuration condition judgment unit is used to determine whether the container meets the resource configuration conditions based on the processed data and the resource configuration policy before configuring the container's resources according to the resource configuration policy corresponding to each container.

[0088] An execution unit is configured to perform the operation of configuring resources for containers according to the resource configuration strategy currently corresponding to each container if the resource configuration conditions are met.

[0089] In some specific embodiments, the resource configuration module 15 may specifically include:

[0090] The priority determination unit is used to determine the resource configuration priority of each container based on the priority of the services on the container.

[0091] The resource configuration unit is used to configure resources for each container according to the resource configuration strategy corresponding to each container and in accordance with the resource configuration priority.

[0092] In some specific embodiments, the in-vehicle multi-container system may specifically include a first container for running an in-vehicle entertainment system, a second container for running a vehicle networking system, and a third container for running a vehicle dashboard;

[0093] Among them, the priority of the car dashboard is higher than the priority of the vehicle networking system, the priority of the vehicle networking system is higher than the priority of the in-vehicle entertainment system; the resource configuration priority of the third container is higher than the resource configuration priority of the second container, and the resource configuration priority of the second container is higher than the resource configuration priority of the first container.

[0094] Furthermore, this application also discloses an electronic device, see [link to relevant documentation]. Figure 9 As shown, the content in the figure should not be considered as any limitation on the scope of use of this application.

[0095] Figure 9 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the vehicle-mounted multi-container system resource configuration method disclosed in any of the foregoing embodiments.

[0096] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0097] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon include operating system 221, computer program 222 and data 223 including system resource usage data, etc. The storage method can be temporary storage or permanent storage.

[0098] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. The operating system 221 can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the vehicle-mounted multi-container system resource configuration method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0099] Furthermore, this application also discloses a computer storage medium storing computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, they implement the resource configuration method steps of the vehicle-mounted multi-container system disclosed in any of the foregoing embodiments.

[0100] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0101] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0102] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0103] The present invention has provided a detailed description of a method, apparatus, device, and storage medium for configuring resources in a vehicle-mounted multi-container system. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for resource allocation in a vehicle-mounted multi-container system, characterized in that, include: Based on the service type of the service carried by the container, the system resource usage data corresponding to the container is obtained using the data acquisition strategy corresponding to the service type. The system resource usage data is analyzed and processed to obtain the processed data; Based on the processed data and the preset rule base, the inference engine is used to determine the current scenario information corresponding to the container; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenarios, and the scenarios are classified according to business type and system load; By querying the pre-built correspondence between scenarios and resource configurations, the resource configuration strategy corresponding to the container under the scenario information is determined; Configure resources for each container according to the resource configuration strategy currently applied to it. Before obtaining the system resource usage data corresponding to the container using the data acquisition strategy corresponding to the business type, the method further includes: By querying the pre-built correspondence between business and resource items, the target resource item corresponding to the business type of the business on the container is determined; The data acquisition strategy is generated based on the target resource item; Before determining the current scene information corresponding to the container using the inference engine based on the processed data and the preset rule base, the method further includes: Acquire historical data from multiple different scenarios; Based on the correspondence between historical data and scenarios, analyze the resource consumption characteristics of different scenarios, and construct scenario recognition rules for different scenarios based on the analysis results; The scene recognition rules are verified, and the scene recognition rules are adjusted if the verification result is unsuccessful. The preset rule library is generated based on the scene recognition rules corresponding to all scenes; The step of performing data analysis and processing on the system resource usage data includes: performing data analysis and processing on all system resource usage data corresponding to the container acquired within a preset time period to obtain processed data; Before configuring resources for each container according to the resource configuration policy currently corresponding to each container, the method further includes: Based on the processed data and the resource configuration strategy, determine whether the container meets the resource configuration conditions; If the resource configuration conditions are met, then the operation of configuring resources for each container according to the current resource configuration strategy for each container is executed.

2. The resource allocation method for a vehicle-mounted multi-container system according to claim 1, characterized in that, The step of obtaining system resource usage data corresponding to the container using a data acquisition strategy corresponding to the business type includes: According to a preset cycle, the system resource usage data corresponding to the container is obtained periodically using a data acquisition strategy corresponding to the business type.

3. The resource allocation method for a vehicle-mounted multi-container system according to claim 1 or 2, characterized in that, The step of configuring resources for containers according to the resource configuration policy currently corresponding to each container includes: Determine the resource configuration priority for each container based on the priority of the services running on the container. Based on the resource configuration strategy corresponding to each container, resources are configured for each container in sequence according to the resource configuration priority.

4. The resource allocation method for a vehicle-mounted multi-container system according to claim 3, characterized in that, The in-vehicle multi-container system includes a first container for running an in-vehicle entertainment system, a second container for running a vehicle networking system, and a third container for running a vehicle dashboard. Among them, the priority of the car dashboard is higher than the priority of the vehicle networking system, the priority of the vehicle networking system is higher than the priority of the in-vehicle entertainment system; the resource configuration priority of the third container is higher than the resource configuration priority of the second container, and the resource configuration priority of the second container is higher than the resource configuration priority of the first container.

5. A vehicle-mounted multi-container system resource allocation device, characterized in that, include: The resource monitoring module is used to obtain system resource usage data corresponding to the container based on the service type of the service carried by the container and using a data acquisition strategy corresponding to the service type. The data analysis and processing module is used to perform data analysis and processing on the system resource usage data to obtain processed data; The scene identification module is used to determine the scene information corresponding to the container based on the processed data and the preset rule base using an inference engine; the preset rule base is a rule base constructed according to the resource consumption characteristics of different scenes, and the scenes are classified according to business type and system load; The resource configuration strategy determination module is used to determine the resource configuration strategy corresponding to the container under the scenario information by querying the pre-built scenario-relationship. The resource configuration module is used to configure resources for containers according to the resource configuration strategy currently applicable to each container. The device is further configured to, before acquiring system resource usage data corresponding to the container using a data acquisition strategy corresponding to the service type, determine the target resource item corresponding to the service type of the service on the container by querying a pre-built correspondence between services and resource items; and generate the data acquisition strategy based on the target resource item. The device is further configured to: acquire historical data from multiple different scenarios before determining the current scenario information of the container using an inference engine based on the processed data and a preset rule base; analyze the resource consumption characteristics of different scenarios according to the correspondence between historical data and scenarios; construct scenario recognition rules for different scenarios based on the analysis results; verify the scenario recognition rules and adjust the scenario recognition rules when the verification result is unsuccessful; and generate the preset rule base based on the scenario recognition rules corresponding to all scenarios. The data analysis and processing module is used to perform data analysis and processing on all system resource usage data corresponding to the container obtained within a preset time period to obtain processed data. The device is further configured to, before configuring the containers according to the resource configuration strategy currently corresponding to each container, determine whether the containers meet the resource configuration conditions based on the processed data and the resource configuration strategy; if the resource configuration conditions are met, then perform the operation of configuring the containers according to the resource configuration strategy currently corresponding to each container.

6. An electronic device, characterized in that, include: Memory is used to store computer programs; A processor for executing the computer program to implement the on-board multi-container system resource configuration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein the computer programs, when executed by a processor, implement the vehicle-mounted multi-container system resource configuration method as described in any one of claims 1 to 4.

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